Ideas:
| Plant | Flowers | Date | lon | lat | ele | Month | Year | julian |
|---|---|---|---|---|---|---|---|---|
| Glossoloma oblongicalyx | 4 | 2015-10-19 | -78.59093 | 0.130838 | 2270 | October | 2015 | 292 |
| Gasteranthus quitensis | 2 | 2016-10-17 | -78.59770 | 0.120070 | 1940 | October | 2016 | 291 |
| Kohleria affinis | 1 | 2016-12-13 | -78.59534 | 0.126746 | 2110 | December | 2016 | 348 |
| Columnea ciliata | 3 | 2014-02-27 | -78.59934 | 0.116682 | 1960 | February | 2014 | 58 |
| Columnea medicinalis | 1 | 2014-04-23 | -78.59372 | 0.128700 | 2130 | April | 2014 | 113 |
| Drymonia teuscheri | 3 | 2016-07-28 | -78.59245 | 0.129393 | 2200 | July | 2016 | 210 |
| Plant | Flowers | Date | lon | lat | ele | Month | Year | julian |
|---|---|---|---|---|---|---|---|---|
| Glossoloma oblongicalyx | 4 | 2015-10-19 | -78.59093 | 0.130838 | 2270 | October | 2015 | 292 |
| Gasteranthus quitensis | 2 | 2016-10-17 | -78.59770 | 0.120070 | 1940 | October | 2016 | 291 |
| Kohleria affinis | 1 | 2016-12-13 | -78.59534 | 0.126746 | 2110 | December | 2016 | 348 |
| Columnea ciliata | 3 | 2014-02-27 | -78.59934 | 0.116682 | 1960 | February | 2014 | 58 |
| Columnea medicinalis | 1 | 2014-04-23 | -78.59372 | 0.128700 | 2130 | April | 2014 | 113 |
| Drymonia teuscheri | 3 | 2016-07-28 | -78.59245 | 0.129393 | 2200 | July | 2016 | 210 |
As range
Equal probability of flowering at anytime.
## sink("model/threshold_baseline.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- alpha[Plant[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<-alpha[PredPlant[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept
## #Intercept flowering count
## alpha[j] ~ dnorm(0,0.386)
##
## }
##
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2324
## Unobserved stochastic nodes: 2926
## Total graph size: 14658
##
## Initializing model
## sink("model/threshold_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Month[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Months){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Autocorrelation priors
## gamma = 10
##
## #Strength of covariance decay
## lambda_cov = 5
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2324
## Unobserved stochastic nodes: 2924
## Total graph size: 18874
##
## Initializing model
Mean phylogenetic covariance
## sink("model/threshold_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Month[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Months){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 5
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2324
## Unobserved stochastic nodes: 2924
## Total graph size: 18872
##
## Initializing model
Mean phylogenetic covariance martix
## sink("model/threshold_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Month[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Months){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Autocorrelation priors
## gamma = 10
##
## #Strength of covariance decay
## lambda_cov = 5
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2324
## Unobserved stochastic nodes: 2924
## Total graph size: 19030
##
## Initializing model
## sink("model/threshold_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Month[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Months){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 5
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2324
## Unobserved stochastic nodes: 2924
## Total graph size: 19028
##
## Initializing model
## sink("model/threshold_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Month[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Months){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Autocorrelation priors
## gamma = 10
##
## #Strength of covariance decay
## lambda_cov = 5
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2324
## Unobserved stochastic nodes: 2924
## Total graph size: 19030
##
## Initializing model
Mean interaction covariance
## sink("model/threshold_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Month[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewMonth[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Months){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 5
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2324
## Unobserved stochastic nodes: 2924
## Total graph size: 19028
##
## Initializing model
Mean interaction covariance martix
Dig into one example.
Glossoloma purpureum
## # A tibble: 7 x 2
## Model p
## <chr> <dbl>
## 1 baseline 0.598
## 2 interaction_attraction 0.151
## 3 interaction_repulsion 1
## 4 phylogenetic_attraction 0.114
## 5 phylogenetic_repulsion 1
## 6 trait_attraction 0.107
## 7 trait_repulsion 1
Without baseline
## # A tibble: 6 x 2
## Model p
## <chr> <dbl>
## 1 interaction_attraction 0.151
## 2 interaction_repulsion 1
## 3 phylogenetic_attraction 0.114
## 4 phylogenetic_repulsion 1
## 5 trait_attraction 0.107
## 6 trait_repulsion 1
| Model | mean | lower | upper |
|---|---|---|---|
| trait_repulsion | 0.3571308 | 0.3493401 | 0.3644422 |
| phylogenetic_repulsion | 0.3433308 | 0.3354418 | 0.3510792 |
| interaction_repulsion | 0.3224660 | 0.3141719 | 0.3307715 |
| interaction_attraction | 0.2269077 | 0.2198348 | 0.2334874 |
| phylogenetic_attraction | 0.2256049 | 0.2187191 | 0.2324988 |
| trait_attraction | 0.2249972 | 0.2183616 | 0.2318602 |
Without baseline
Zoom in
| Model | mean | lower | upper |
|---|---|---|---|
| trait_repulsion | 0.3675204 | 0.3333333 | 0.4013605 |
| phylogenetic_repulsion | 0.3538197 | 0.3214286 | 0.3860544 |
| interaction_repulsion | 0.3315697 | 0.2993197 | 0.3656463 |
| baseline | 0.2475867 | 0.2210884 | 0.2738095 |
| interaction_attraction | 0.2301684 | 0.2057823 | 0.2534014 |
| phylogenetic_attraction | 0.2294507 | 0.2057823 | 0.2551020 |
| trait_attraction | 0.2284728 | 0.2039966 | 0.2534014 |
| Plant | Month | Observed | baseline | interaction_attraction | interaction_repulsion | phylogenetic_attraction | phylogenetic_repulsion | trait_attraction | trait_repulsion |
|---|---|---|---|---|---|---|---|---|---|
| Besleria solanoides | 1 | 0.0 | 16.2 | 3.1 | 19.5 | 3.5 | 15.8 | 5.3 | 18.0 |
| Besleria solanoides | 2 | 0.0 | 17.0 | 2.5 | 16.8 | 2.3 | 14.2 | 3.8 | 19.4 |
| Besleria solanoides | 3 | 0.0 | 16.6 | 2.6 | 18.4 | 3.3 | 17.3 | 3.2 | 22.4 |
| Besleria solanoides | 4 | 13.3 | 16.8 | 13.0 | 27.2 | 14.7 | 23.4 | 13.3 | 30.1 |
| Besleria solanoides | 5 | 11.1 | 16.2 | 11.4 | 24.4 | 12.6 | 21.0 | 10.6 | 29.1 |
| Besleria solanoides | 6 | 10.0 | 16.9 | 10.1 | 22.1 | 11.2 | 18.6 | 9.4 | 27.2 |
| Besleria solanoides | 7 | 6.7 | 15.6 | 8.4 | 21.7 | 8.5 | 18.8 | 7.2 | 26.2 |
| Besleria solanoides | 8 | 8.3 | 16.5 | 9.4 | 25.2 | 11.0 | 22.3 | 11.3 | 28.9 |
| Besleria solanoides | 9 | 46.7 | 16.3 | 46.1 | 51.9 | 47.0 | 49.5 | 44.4 | 57.2 |
| Besleria solanoides | 10 | 57.1 | 16.7 | 56.8 | 61.2 | 57.3 | 55.7 | 55.1 | 64.1 |
| Besleria solanoides | 11 | 33.3 | 16.9 | 33.8 | 43.3 | 34.0 | 40.8 | 31.3 | 49.1 |
| Besleria solanoides | 12 | 12.5 | 16.0 | 13.6 | 32.6 | 15.1 | 27.5 | 12.8 | 35.1 |
| Columnea ciliata | 1 | 25.0 | 17.5 | 25.0 | 37.9 | 23.4 | 42.4 | 24.9 | 42.5 |
| Columnea ciliata | 2 | 43.8 | 17.8 | 42.6 | 53.0 | 42.2 | 56.4 | 42.0 | 54.5 |
| Columnea ciliata | 3 | 66.7 | 17.6 | 64.4 | 71.6 | 62.4 | 77.5 | 63.2 | 74.1 |
| Columnea ciliata | 4 | 46.7 | 17.7 | 45.7 | 55.5 | 44.7 | 61.4 | 45.9 | 58.6 |
| Columnea ciliata | 5 | 5.6 | 17.8 | 6.5 | 21.3 | 7.2 | 23.6 | 7.0 | 23.3 |
| Columnea ciliata | 6 | 10.0 | 18.0 | 10.3 | 23.1 | 11.0 | 25.1 | 10.4 | 24.6 |
| Columnea ciliata | 7 | 6.7 | 17.9 | 7.7 | 23.6 | 8.3 | 25.3 | 8.1 | 23.4 |
| Columnea ciliata | 8 | 0.0 | 18.4 | 3.1 | 21.4 | 3.1 | 24.7 | 3.1 | 22.1 |
| Columnea ciliata | 9 | 0.0 | 17.4 | 2.8 | 18.2 | 1.9 | 24.1 | 2.2 | 22.1 |
| Columnea ciliata | 10 | 0.0 | 18.4 | 2.5 | 19.6 | 1.8 | 24.8 | 2.2 | 22.2 |
| Columnea ciliata | 11 | 0.0 | 17.6 | 3.6 | 24.2 | 2.7 | 30.6 | 3.6 | 28.6 |
| Columnea ciliata | 12 | 0.0 | 17.7 | 3.5 | 27.7 | 2.8 | 33.4 | 3.0 | 30.6 |
| Columnea kucyniakii | 1 | 25.0 | 9.9 | 25.4 | 35.9 | 25.1 | 39.8 | 24.8 | 43.9 |
| Columnea kucyniakii | 2 | 43.8 | 9.6 | 42.7 | 48.7 | 43.2 | 54.2 | 41.7 | 57.7 |
| Columnea kucyniakii | 3 | 16.7 | 9.4 | 17.2 | 29.6 | 17.3 | 34.4 | 15.5 | 38.7 |
| Columnea kucyniakii | 4 | 6.7 | 9.5 | 7.7 | 20.3 | 7.9 | 26.2 | 8.3 | 26.3 |
| Columnea kucyniakii | 5 | 0.0 | 9.8 | 2.6 | 14.0 | 2.9 | 18.0 | 3.4 | 18.8 |
| Columnea kucyniakii | 6 | 0.0 | 9.2 | 2.3 | 13.6 | 2.5 | 16.7 | 3.2 | 18.5 |
| Columnea kucyniakii | 7 | 0.0 | 9.3 | 2.7 | 16.0 | 3.3 | 19.6 | 2.2 | 23.9 |
| Columnea kucyniakii | 8 | 0.0 | 9.2 | 3.5 | 18.1 | 3.0 | 22.5 | 2.9 | 25.4 |
| Columnea kucyniakii | 9 | 0.0 | 9.8 | 2.7 | 16.7 | 2.4 | 21.6 | 2.8 | 24.2 |
| Columnea kucyniakii | 10 | 0.0 | 9.6 | 2.7 | 16.5 | 1.8 | 22.3 | 1.6 | 26.8 |
| Columnea kucyniakii | 11 | 11.1 | 9.6 | 13.0 | 29.3 | 11.3 | 35.6 | 8.9 | 40.0 |
| Columnea kucyniakii | 12 | 12.5 | 9.1 | 14.2 | 30.5 | 13.5 | 38.8 | 11.8 | 39.8 |
| Columnea medicinalis | 1 | 16.7 | 18.3 | 17.6 | 32.3 | 17.9 | 35.4 | 16.9 | 33.6 |
| Columnea medicinalis | 2 | 12.5 | 18.4 | 12.8 | 27.6 | 12.8 | 30.0 | 13.1 | 27.3 |
| Columnea medicinalis | 3 | 25.0 | 19.0 | 23.9 | 38.4 | 23.6 | 44.9 | 23.5 | 40.8 |
| Columnea medicinalis | 4 | 6.7 | 18.3 | 7.3 | 23.1 | 7.5 | 28.4 | 6.7 | 25.4 |
| Columnea medicinalis | 5 | 11.1 | 18.4 | 11.5 | 26.1 | 12.0 | 27.2 | 11.8 | 26.0 |
| Columnea medicinalis | 6 | 30.0 | 18.4 | 29.3 | 40.3 | 29.3 | 41.4 | 30.9 | 39.2 |
| Columnea medicinalis | 7 | 33.3 | 18.4 | 32.3 | 45.7 | 32.3 | 49.0 | 32.1 | 44.6 |
| Columnea medicinalis | 8 | 25.0 | 17.9 | 23.6 | 40.4 | 23.7 | 44.9 | 25.4 | 40.6 |
| Columnea medicinalis | 9 | 6.7 | 18.2 | 7.8 | 24.8 | 6.8 | 29.6 | 6.1 | 27.0 |
| Columnea medicinalis | 10 | 28.6 | 18.4 | 26.9 | 42.5 | 25.6 | 49.7 | 25.8 | 45.1 |
| Columnea medicinalis | 11 | 11.1 | 18.5 | 11.2 | 32.8 | 9.1 | 39.4 | 9.8 | 36.9 |
| Columnea medicinalis | 12 | 0.0 | 18.0 | 3.1 | 27.8 | 3.6 | 32.4 | 2.7 | 28.8 |
| Columnea picta | 1 | 25.0 | 16.4 | 26.2 | 37.3 | 24.3 | 43.2 | 25.2 | 42.0 |
| Columnea picta | 2 | 12.5 | 16.0 | 13.2 | 27.9 | 12.9 | 29.6 | 13.4 | 26.9 |
| Columnea picta | 3 | 0.0 | 16.3 | 3.0 | 19.8 | 3.3 | 22.7 | 3.4 | 22.7 |
| Columnea picta | 4 | 13.3 | 16.9 | 13.7 | 29.2 | 13.6 | 32.7 | 13.2 | 32.0 |
| Columnea picta | 5 | 33.3 | 15.7 | 32.5 | 43.6 | 31.6 | 48.5 | 32.5 | 48.2 |
| Columnea picta | 6 | 35.0 | 16.2 | 34.6 | 44.4 | 34.2 | 46.3 | 32.5 | 48.2 |
| Columnea picta | 7 | 13.3 | 16.4 | 14.4 | 27.8 | 14.2 | 29.8 | 12.8 | 32.8 |
| Columnea picta | 8 | 8.3 | 16.2 | 8.9 | 27.2 | 9.4 | 30.4 | 7.9 | 30.4 |
| Columnea picta | 9 | 13.3 | 15.7 | 14.2 | 28.8 | 12.4 | 34.4 | 12.5 | 34.2 |
| Columnea picta | 10 | 0.0 | 16.9 | 2.6 | 19.2 | 2.9 | 23.6 | 1.8 | 24.1 |
| Columnea picta | 11 | 0.0 | 16.5 | 3.2 | 25.5 | 3.4 | 29.8 | 4.0 | 29.4 |
| Columnea picta | 12 | 12.5 | 15.8 | 12.7 | 34.2 | 9.4 | 41.8 | 13.5 | 38.5 |
| Columnea strigosa | 1 | 8.3 | 13.9 | 10.5 | 21.9 | 10.0 | 28.3 | 10.6 | 27.2 |
| Columnea strigosa | 2 | 12.5 | 13.2 | 13.8 | 22.0 | 13.7 | 28.8 | 13.8 | 26.4 |
| Columnea strigosa | 3 | 0.0 | 13.8 | 3.5 | 16.4 | 4.1 | 20.3 | 4.4 | 18.5 |
| Columnea strigosa | 4 | 0.0 | 13.2 | 2.7 | 13.7 | 3.3 | 20.6 | 2.5 | 19.2 |
| Columnea strigosa | 5 | 22.2 | 13.9 | 23.2 | 28.6 | 21.8 | 38.1 | 21.4 | 36.0 |
| Columnea strigosa | 6 | 40.0 | 13.4 | 40.5 | 42.1 | 38.5 | 51.2 | 40.2 | 47.5 |
| Columnea strigosa | 7 | 26.7 | 13.4 | 27.3 | 33.4 | 25.8 | 42.2 | 26.4 | 39.1 |
| Columnea strigosa | 8 | 16.7 | 13.5 | 18.4 | 28.2 | 15.8 | 37.6 | 17.1 | 33.6 |
| Columnea strigosa | 9 | 0.0 | 13.3 | 2.9 | 14.4 | 1.5 | 23.3 | 2.2 | 21.2 |
| Columnea strigosa | 10 | 0.0 | 13.5 | 2.9 | 14.9 | 1.9 | 24.6 | 3.0 | 19.9 |
| Columnea strigosa | 11 | 0.0 | 12.8 | 4.0 | 20.3 | 2.5 | 31.6 | 2.9 | 25.6 |
| Columnea strigosa | 12 | 0.0 | 12.6 | 4.5 | 20.9 | 3.0 | 32.0 | 2.3 | 28.9 |
| Drymonia collegarum | 1 | 16.7 | 14.8 | 17.3 | 31.4 | 17.0 | 35.1 | 17.2 | 35.5 |
| Drymonia collegarum | 2 | 6.2 | 14.1 | 7.4 | 21.8 | 7.1 | 26.5 | 6.6 | 28.1 |
| Drymonia collegarum | 3 | 8.3 | 14.5 | 9.5 | 24.5 | 10.7 | 28.3 | 11.6 | 30.2 |
| Drymonia collegarum | 4 | 13.3 | 14.4 | 14.6 | 26.8 | 15.2 | 32.4 | 14.3 | 32.7 |
| Drymonia collegarum | 5 | 22.2 | 14.3 | 21.7 | 33.4 | 22.2 | 34.5 | 23.6 | 36.0 |
| Drymonia collegarum | 6 | 15.0 | 14.5 | 15.2 | 25.9 | 15.4 | 29.2 | 15.3 | 31.9 |
| Drymonia collegarum | 7 | 13.3 | 14.2 | 14.2 | 27.4 | 14.0 | 32.1 | 14.1 | 33.8 |
| Drymonia collegarum | 8 | 16.7 | 15.2 | 17.5 | 31.7 | 17.2 | 36.4 | 17.2 | 36.8 |
| Drymonia collegarum | 9 | 13.3 | 14.4 | 14.5 | 28.5 | 11.9 | 35.2 | 13.2 | 34.7 |
| Drymonia collegarum | 10 | 7.1 | 14.3 | 9.1 | 22.9 | 8.6 | 30.2 | 8.8 | 28.2 |
| Drymonia collegarum | 11 | 11.1 | 14.0 | 11.0 | 31.1 | 10.4 | 38.1 | 11.4 | 37.4 |
| Drymonia collegarum | 12 | 25.0 | 13.9 | 23.5 | 43.2 | 17.7 | 52.8 | 19.7 | 50.2 |
| Drymonia tenuis | 1 | 16.7 | 15.1 | 17.8 | 30.1 | 16.7 | 34.9 | 18.4 | 34.4 |
| Drymonia tenuis | 2 | 18.8 | 14.6 | 19.1 | 29.8 | 18.8 | 36.4 | 18.0 | 37.9 |
| Drymonia tenuis | 3 | 25.0 | 14.7 | 25.7 | 36.8 | 25.2 | 41.7 | 22.3 | 45.8 |
| Drymonia tenuis | 4 | 13.3 | 14.9 | 14.6 | 26.4 | 13.9 | 30.6 | 14.2 | 33.8 |
| Drymonia tenuis | 5 | 22.2 | 14.8 | 22.4 | 31.4 | 22.2 | 35.2 | 20.4 | 39.8 |
| Drymonia tenuis | 6 | 20.0 | 15.1 | 20.7 | 29.4 | 19.9 | 33.6 | 19.1 | 37.7 |
| Drymonia tenuis | 7 | 20.0 | 15.3 | 20.2 | 30.0 | 20.6 | 36.1 | 16.6 | 41.8 |
| Drymonia tenuis | 8 | 8.3 | 15.3 | 9.4 | 24.8 | 9.2 | 29.0 | 10.1 | 31.2 |
| Drymonia tenuis | 9 | 6.7 | 14.5 | 7.7 | 21.0 | 7.2 | 26.2 | 8.5 | 26.9 |
| Drymonia tenuis | 10 | 7.1 | 14.7 | 8.0 | 22.5 | 8.3 | 27.9 | 10.1 | 25.7 |
| Drymonia tenuis | 11 | 0.0 | 13.8 | 2.9 | 23.2 | 3.9 | 27.8 | 7.9 | 22.9 |
| Drymonia tenuis | 12 | 0.0 | 14.3 | 4.1 | 24.5 | 3.9 | 29.6 | 6.8 | 27.7 |
| Drymonia teuscheri | 1 | 16.7 | 17.8 | 18.1 | 28.2 | 16.7 | 37.0 | 17.6 | 35.8 |
| Drymonia teuscheri | 2 | 6.2 | 18.1 | 7.7 | 19.1 | 7.3 | 26.4 | 7.0 | 27.3 |
| Drymonia teuscheri | 3 | 25.0 | 18.3 | 25.2 | 33.9 | 22.4 | 44.8 | 22.7 | 46.5 |
| Drymonia teuscheri | 4 | 26.7 | 18.0 | 26.9 | 35.7 | 24.9 | 43.5 | 25.6 | 44.3 |
| Drymonia teuscheri | 5 | 33.3 | 17.8 | 33.3 | 39.5 | 32.6 | 47.4 | 32.4 | 47.8 |
| Drymonia teuscheri | 6 | 20.0 | 17.9 | 21.1 | 28.5 | 19.6 | 35.3 | 19.5 | 37.1 |
| Drymonia teuscheri | 7 | 13.3 | 17.6 | 14.4 | 25.4 | 14.5 | 31.9 | 13.5 | 33.9 |
| Drymonia teuscheri | 8 | 25.0 | 18.3 | 25.9 | 35.2 | 22.7 | 44.4 | 23.4 | 43.4 |
| Drymonia teuscheri | 9 | 6.7 | 18.7 | 8.0 | 20.9 | 7.8 | 28.0 | 8.2 | 27.4 |
| Drymonia teuscheri | 10 | 14.3 | 17.9 | 14.9 | 26.2 | 12.9 | 37.3 | 13.6 | 34.6 |
| Drymonia teuscheri | 11 | 11.1 | 18.3 | 12.4 | 28.6 | 11.5 | 38.3 | 11.7 | 36.7 |
| Drymonia teuscheri | 12 | 0.0 | 18.1 | 4.4 | 22.6 | 6.7 | 28.9 | 6.2 | 27.7 |
| Gasteranthus lateralis | 1 | 33.3 | 15.4 | 33.1 | 41.6 | 33.6 | 39.0 | 31.8 | 50.2 |
| Gasteranthus lateralis | 2 | 12.5 | 15.6 | 13.4 | 25.4 | 13.3 | 23.4 | 13.1 | 33.2 |
| Gasteranthus lateralis | 3 | 8.3 | 15.5 | 9.9 | 24.0 | 9.7 | 24.3 | 9.8 | 31.0 |
| Gasteranthus lateralis | 4 | 13.3 | 15.5 | 14.1 | 25.2 | 14.3 | 25.2 | 13.4 | 32.7 |
| Gasteranthus lateralis | 5 | 5.6 | 15.8 | 5.7 | 19.0 | 6.7 | 17.9 | 7.5 | 23.4 |
| Gasteranthus lateralis | 6 | 5.0 | 15.4 | 5.9 | 17.5 | 6.2 | 16.2 | 7.5 | 23.6 |
| Gasteranthus lateralis | 7 | 0.0 | 15.6 | 2.7 | 16.5 | 2.1 | 15.9 | 4.3 | 22.4 |
| Gasteranthus lateralis | 8 | 16.7 | 15.9 | 17.4 | 30.3 | 17.2 | 27.8 | 16.9 | 38.2 |
| Gasteranthus lateralis | 9 | 13.3 | 15.3 | 13.6 | 26.7 | 13.9 | 24.3 | 13.1 | 32.4 |
| Gasteranthus lateralis | 10 | 28.6 | 15.5 | 29.0 | 38.0 | 29.9 | 34.2 | 26.4 | 44.6 |
| Gasteranthus lateralis | 11 | 44.4 | 15.8 | 42.3 | 52.0 | 44.1 | 49.8 | 37.3 | 64.1 |
| Gasteranthus lateralis | 12 | 25.0 | 15.0 | 24.2 | 40.3 | 26.0 | 38.5 | 21.2 | 51.1 |
| Gasteranthus quitensis | 1 | 41.7 | 12.5 | 40.6 | 47.5 | 41.1 | 46.3 | 38.3 | 54.6 |
| Gasteranthus quitensis | 2 | 18.8 | 12.6 | 19.7 | 30.6 | 19.6 | 27.8 | 17.0 | 33.7 |
| Gasteranthus quitensis | 3 | 8.3 | 11.7 | 10.1 | 23.8 | 9.5 | 23.1 | 8.7 | 28.6 |
| Gasteranthus quitensis | 4 | 6.7 | 13.3 | 7.9 | 20.4 | 8.4 | 19.9 | 8.6 | 23.8 |
| Gasteranthus quitensis | 5 | 0.0 | 12.6 | 2.4 | 15.4 | 2.2 | 14.7 | 2.6 | 17.5 |
| Gasteranthus quitensis | 6 | 0.0 | 12.5 | 2.2 | 13.9 | 2.1 | 12.7 | 2.8 | 17.3 |
| Gasteranthus quitensis | 7 | 6.7 | 12.2 | 7.6 | 21.0 | 7.9 | 20.6 | 7.4 | 24.9 |
| Gasteranthus quitensis | 8 | 16.7 | 12.7 | 17.8 | 29.9 | 17.3 | 28.2 | 16.8 | 32.5 |
| Gasteranthus quitensis | 9 | 13.3 | 12.4 | 14.1 | 27.0 | 14.3 | 24.0 | 15.7 | 24.4 |
| Gasteranthus quitensis | 10 | 21.4 | 12.2 | 22.4 | 32.4 | 22.4 | 30.3 | 23.0 | 29.4 |
| Gasteranthus quitensis | 11 | 11.1 | 12.3 | 12.1 | 29.1 | 14.1 | 25.7 | 15.0 | 27.9 |
| Gasteranthus quitensis | 12 | 12.5 | 13.0 | 14.5 | 31.4 | 15.5 | 28.0 | 13.7 | 32.9 |
| Glossoloma oblongicalyx | 1 | 0.0 | 15.2 | 3.4 | 19.6 | 4.1 | 23.2 | 3.6 | 23.3 |
| Glossoloma oblongicalyx | 2 | 0.0 | 15.4 | 2.6 | 17.6 | 2.2 | 19.9 | 3.2 | 19.4 |
| Glossoloma oblongicalyx | 3 | 0.0 | 15.5 | 3.2 | 19.2 | 2.5 | 23.4 | 3.3 | 22.4 |
| Glossoloma oblongicalyx | 4 | 0.0 | 15.4 | 2.6 | 16.6 | 2.5 | 22.2 | 3.5 | 19.7 |
| Glossoloma oblongicalyx | 5 | 11.1 | 15.5 | 11.4 | 23.4 | 10.7 | 28.0 | 11.1 | 29.9 |
| Glossoloma oblongicalyx | 6 | 10.0 | 15.6 | 10.7 | 21.6 | 10.5 | 25.2 | 10.2 | 28.0 |
| Glossoloma oblongicalyx | 7 | 33.3 | 15.4 | 32.7 | 42.3 | 33.5 | 46.2 | 32.5 | 50.4 |
| Glossoloma oblongicalyx | 8 | 33.3 | 15.2 | 33.5 | 43.4 | 33.3 | 48.9 | 30.8 | 52.3 |
| Glossoloma oblongicalyx | 9 | 33.3 | 15.5 | 33.0 | 41.8 | 32.6 | 47.4 | 32.1 | 50.9 |
| Glossoloma oblongicalyx | 10 | 35.7 | 15.5 | 34.5 | 43.2 | 34.3 | 50.5 | 32.0 | 54.3 |
| Glossoloma oblongicalyx | 11 | 22.2 | 15.3 | 22.6 | 38.0 | 21.0 | 43.4 | 18.7 | 49.0 |
| Glossoloma oblongicalyx | 12 | 0.0 | 14.8 | 4.3 | 24.8 | 3.8 | 28.3 | 3.8 | 32.0 |
| Glossoloma purpureum | 1 | 16.7 | 13.7 | 17.7 | 31.7 | 15.8 | 35.9 | 15.7 | 37.9 |
| Glossoloma purpureum | 2 | 12.5 | 13.2 | 12.9 | 25.6 | 12.3 | 30.0 | 11.2 | 30.8 |
| Glossoloma purpureum | 3 | 0.0 | 13.1 | 2.5 | 20.2 | 2.5 | 22.9 | 2.6 | 23.3 |
| Glossoloma purpureum | 4 | 6.7 | 12.9 | 7.6 | 22.5 | 6.7 | 27.1 | 7.1 | 26.9 |
| Glossoloma purpureum | 5 | 5.6 | 13.1 | 6.5 | 20.2 | 6.5 | 22.8 | 6.4 | 23.9 |
| Glossoloma purpureum | 6 | 10.0 | 13.0 | 10.5 | 23.2 | 10.3 | 25.0 | 10.0 | 26.9 |
| Glossoloma purpureum | 7 | 20.0 | 12.8 | 19.7 | 33.1 | 20.4 | 34.4 | 21.5 | 36.3 |
| Glossoloma purpureum | 8 | 16.7 | 12.9 | 16.7 | 31.9 | 17.7 | 33.5 | 18.1 | 35.2 |
| Glossoloma purpureum | 9 | 26.7 | 13.0 | 26.9 | 37.3 | 26.1 | 42.1 | 26.5 | 42.7 |
| Glossoloma purpureum | 10 | 14.3 | 12.8 | 14.5 | 28.7 | 15.1 | 32.0 | 14.9 | 34.3 |
| Glossoloma purpureum | 11 | 11.1 | 12.8 | 11.8 | 31.2 | 12.0 | 33.6 | 12.7 | 35.4 |
| Glossoloma purpureum | 12 | 12.5 | 13.1 | 14.2 | 34.1 | 12.6 | 39.4 | 11.0 | 39.3 |
| Kohleria affinis | 1 | 8.3 | 17.3 | 10.3 | 22.1 | 9.7 | 21.5 | 9.2 | 30.9 |
| Kohleria affinis | 2 | 25.0 | 17.1 | 25.3 | 33.0 | 25.7 | 31.6 | 24.7 | 39.7 |
| Kohleria affinis | 3 | 41.7 | 17.4 | 40.6 | 46.6 | 42.5 | 45.2 | 39.5 | 56.2 |
| Kohleria affinis | 4 | 46.7 | 17.6 | 45.6 | 50.8 | 47.0 | 48.0 | 45.5 | 60.2 |
| Kohleria affinis | 5 | 27.8 | 17.1 | 27.5 | 35.0 | 28.4 | 33.2 | 27.3 | 44.5 |
| Kohleria affinis | 6 | 15.0 | 17.0 | 15.6 | 24.1 | 15.7 | 22.7 | 14.4 | 31.8 |
| Kohleria affinis | 7 | 0.0 | 17.5 | 2.3 | 15.5 | 3.0 | 14.7 | 2.2 | 22.4 |
| Kohleria affinis | 8 | 8.3 | 17.1 | 10.2 | 22.3 | 10.7 | 21.5 | 8.2 | 32.7 |
| Kohleria affinis | 9 | 6.7 | 17.3 | 8.0 | 20.4 | 8.2 | 18.5 | 6.8 | 28.4 |
| Kohleria affinis | 10 | 0.0 | 16.9 | 3.3 | 16.2 | 2.6 | 14.5 | 2.2 | 24.9 |
| Kohleria affinis | 11 | 0.0 | 17.7 | 4.0 | 20.4 | 3.8 | 18.4 | 3.7 | 30.4 |
| Kohleria affinis | 12 | 12.5 | 17.2 | 13.5 | 28.2 | 14.8 | 27.4 | 11.8 | 41.0 |